This invention relates to telecommunications, and more particularly to a signal recognizer that receives a signal of unknown type and determines its signal parameters.
Signal demodulation assumes that certain signal parameters, such as modulation type, are known. Thus, conventional signal demodulation is achieved with some sort of hardware device, designed to receive a particular type of signal. The demodulator operates only on the type of signal it is designed to receive. For example, an FM demodulator operates on FM signals, and must be tuned to a frequency appropriate for an incoming signal.
Identification of signals of an unknown type has evolved independently of demodulation techniques. Examples of applications of signal identification include direction finding confirmation, monitoring, spectrum management, interference identification, and electronic surveillance. Various techniques have been developed for xe2x80x9cautomatic signal recognitionxe2x80x9d, which seeks to identify the modulation type (along with various parameters such as baud rate) of a detected signal for the purpose of signal exploitation. For example, a signal recognizer can be used to extract signal information useful for choosing a suitable counter measure, such as jamming.
Interest in modulation identification algorithms has increased with the emergence of new communication technologies. In particular, there is growing interest in algorithms that recognize quadrature amplitude modulated (QAM) signals, which are used in the HF, VHF, and UHF bands for a wide variety of applications including FAX, modem, and digital cellular.
Many techniques for modulation recognition have been published in the literature. In early work, frequency-domain parameters were used to distinguish between six candidate modulation types. This work was published in a report by Weaver, Cole, Krumland, and Miller entitled The Automatic Classification of Modulation Types by Pattern Recognition, Stanford Electronics Laboratories, Technical Report No. 1829-2, April 1969. A well-known paper treating digital modulation types presents results based on a statistical analysis of various signal parameters to discriminate between amplitude shift keying (ASK), FSK, and PSK. F. F. Liedtke, xe2x80x9cComputer Simulation of an Automatic Classification Procedure for Digitally Modulated Communications Signals with Unknown Parametersxe2x80x9d, Signal Processing, Vol. 6, pp. 311-23, 1984. In general, the parameter-based approaches were directed to detectable parameters of the signal, such as its envelope or phase.
Other modulation recognition methods use a combination of techniques, including pattern recognition. Higher-order statistics have exploited cyclostationarity to identify modulation. Other methods have applied neural networks. A recent book by Azzouz and Nandi, Automatic Signal Recognition of Communications Signals (Kluwer, 1997), gives details on these and other recent techniques for identifying modulation types.
A more recent approach to modulation recognition is to apply techniques from maximum-likelihood (ML) decision theory. A truncated infinite series to approximate likelihood functions is described by Long, et al, xe2x80x9cFurther Results in Likelihood Classification of QAM Signalsxe2x80x9d, Proceedings of MILCOM-94, pp. 57-61, October 1994. A technique that uses demodulated BPSK and QPSK symbols, but assumes knowledge of carrier frequency and phase is described by Sampiano and Martin, xe2x80x9cMaximum Likelihood PSK Classifierxe2x80x9d, MILCOM-96, pp 1010-14, 1996. A comprehensive review of the literature on signal classification, which includes methods based on ML decision theory, is presented in Boiteau and Le Martret, A Generalized Maximum Likelihood Framework for Modulation Classification, International Conference on Acoustics, Speech, and Signal Processing, 1998.
One embodiment of the invention is a computer-implemented signal recognizer for classifying noncooperative signals. An up/down detector detects the presence of a signal of interest. A signal classifier has a number of classifier modules, each module associated with a different signal modulation type. Each module is operable to perform the following tasks: to receive the detected signal in digital form, to estimate parameters of said signal, to demodulate said signal based on estimated parameters, to determine a candidate signal type having said estimated parameters, and to calculate confidence data representing the extent to which said signal is likely of said candidate signal type versus not of that signal type. A confidence analyzer receives confidence data from each module and determines a best signal type from the candidate signal types determined by the modules. A graphical user interface may be used to control tasking and to view the status and the results of signal recognition process.
An advantage of the invention is that it detects and demodulates a signal that may be any one of a number of unknown signal types. These signals include analog signals, such as AM, FM, USB, LSB, and digital signals, such as OOK, ASK, FSK, MSK, PSK, and QAM. Thus, a wide variety of signal types can be recognized, including all widely used digital communication signals.
All signal parameter detection and analysis is performed with software. The software may be executed on various hardware platforms, including general purpose processor systems. The signal recognizer may be used as a stand-alone device, remotely tasked, or integrated into narrowband or wideband systems.
In contrast to a set of dedicated hardware demodulators, which each independently attempt to demodulate an incoming signal according to configured parameters, the signal recognizer first processes the signal to determine its signal parameters. It then demodulates the signal according to the estimated parameters. To estimate the parameters, a hypothesis testing approach is used, but in contrast to other signal recognition techniques, the invention accommodates a wide variety of signal types. A xe2x80x9cfalse alarmxe2x80x9d test evaluates each candidate signal against the likelihood of it not being that signal.
The invention provides classification results with a minimum probability of error. It operates in environments characterized by multipath and low signal to noise ratios.